Securing Internet of Things (IoT) with machine learning

Sherali Zeadally, Michail Tsikerdekis

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet-connected devices are part of the IoT ecosystem. IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host-based and network-based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices.

Original languageEnglish
Article numbere4169
JournalInternational Journal of Communication Systems
Volume33
Issue number1
DOIs
StatePublished - Jan 10 2020

Bibliographical note

Publisher Copyright:
© 2019 John Wiley & Sons, Ltd.

Keywords

  • attack
  • internet of things
  • machine learning
  • security

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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